Researchon Multi Parameter Coupled Learning Model for Landslide Monitoring and Early-warning
Monitoring and early warning of landslide is an important work in the field of geological hazard monitoring along oil and gas pipelines.However,most of the methordsuesd in traditional monitoring and early warning are based on experience to set the threshold value,lacking the analysis and refinement of monitoring data,which is easy to cause the threshold setting too high or too low situation.In order to solve such problems,a new landslide warning model is constructed based on Gaussian Mixture Model(GMM)through the analysis of monitoring data.Firstly,on the basis of data outlier elimination and missing value complement,Principal Component Analysis(PCA)analysis method was used to extract variable features of landslide monitoring data and form spatial characteristic variables of new dimensions.Secondly,a multi-parameter coupled landslide warning model was constructed using GMM and combining variable features.Finally,the model is applied to the real data of landslide monitoring,so as to realize the verification of the early warning model.The application results show that the model can extract the composite advanced features of landslide monitoring data and realize the early warning of landslide.
万方数据
维普
